test: add some test cases for the python module WordMatching

cosmic_ray_config.toml

@@ -1,8 +1,8 @@
 [cosmic-ray]
-module-path = "aip_trainer/lambdas/lambdaSpeechToScore.py"
+module-path = "aip_trainer/WordMatching.py"
 timeout = 30.0
 excluded-modules = []
-test-command = "python -m pytest tests/lambdas/test_lambdaSpeechToScore.py tests/lambdas/test_lambdaSpeechToScore_librosa.py"
+test-command = "python -m pytest tests/test_worldmatching.py"
 
 [cosmic-ray.distributor]
 name = "local"

tests/test_worldmatching.py

@@ -0,0 +1,49 @@
+import unittest
+import numpy as np
+from aip_trainer import WordMatching
+
+
+class TestWordMatching(unittest.TestCase):
+
+    def test_get_word_distance_matrix(self):
+        words_estimated = ["hello", "world"]
+        words_real = ["hello", "word"]
+        expected_matrix = np.array([[0.0, 5.0], [4.0, 1.0], [5.0, 4.0]])
+        result_matrix = WordMatching.get_word_distance_matrix(words_estimated, words_real)
+        np.testing.assert_array_equal(result_matrix, expected_matrix)
+
+    def test_get_best_path_from_distance_matrix(self):
+        word_distance_matrix = np.array([[0, 4], [5, 1], [5, 4]])
+        expected_indices = np.array([0, 1])
+        result_indices = WordMatching.get_best_path_from_distance_matrix(word_distance_matrix)
+        np.testing.assert_array_equal(result_indices, expected_indices)
+
+    def test_get_resulting_string(self):
+        mapped_indices = np.array([0, 1])
+        words_estimated = ["hello", "world"]
+        words_real = ["hello", "word"]
+        expected_words = ["hello", "world"]
+        expected_indices = [0, 1]
+        result_words, result_indices = WordMatching.get_resulting_string(mapped_indices, words_estimated, words_real)
+        self.assertEqual(result_words, expected_words)
+        self.assertEqual(result_indices, expected_indices)
+
+    def test_getWhichLettersWereTranscribedCorrectly(self):
+        real_word = "hello"
+        transcribed_word = "hxllo"
+        expected_result = [1, 0, 1, 1, 1]
+        result = WordMatching.getWhichLettersWereTranscribedCorrectly(real_word, transcribed_word)
+        self.assertEqual(result, expected_result)
+
+    def test_get_best_mapped_words(self):
+        words_estimated = ["hello", "world"]
+        words_real = ["hello", "word"]
+        expected_words = ["hello", "world"]
+        expected_indices = [0, 1]
+        result_words, result_indices = WordMatching.get_best_mapped_words(words_estimated, words_real)
+        self.assertEqual(result_words, expected_words)
+        self.assertEqual(result_indices, expected_indices)
+
+
+if __name__ == '__main__':
+    unittest.main()